Why Are Weather Forecasts Often Wrong?

Scientists use mathematical models for everything from weather forecasts to predicting who will win the next election. But how do these models work and why do they sometimes get it wrong? Everyday Einstein looks at the science behind weather prediction.

Last week, I went on a three-mile bike ride with my kids. We were on our way home when the skies started to look dark. We weren't worried however, because it was only 3 PM and the weatherman said it wouldn't start raining until after 7 PM and even then we could expect just some light showers. Unfortunately, he must have forgotten to mention this to the clouds because a slow drizzle started to fall as we rode. Soon the drizzle turned into a downpour, complete with hail and lightning. We eventually made it back to our warm dry house, but the soaked children voiced several complaints about the forecast and vowed never to trust the weatherman again. So just how do scientists predict something as complicated as the weather, and why are they so often wrong about it?

I’m a Model, You Know What I Mean

In order to predict things like the weather, climate change, or even election results, scientists use a tool called a mathematical model. A mathematical model is a set of equations that can predict an outcome based on a set of inputs. Let's look at an example to see just what that means.

Imagine that you have 5 kids and each morning they all want juice for breakfast. Everyday from Monday through Thursday, you get 3 requests for apple juice and 2 requests for orange juice. Knowing this, you could write a set of mathematical equations to tell you how much juice you need of each type on a given day. The equations might say:

Orange Juice = 2/5 x Number of Kids

and

Apple Juice = 3/5 x Number of Kids

So now imagine that Friday morning roles around and I'm making breakfast. Since I have my model in hand, I can take my inputs (the number of kids) and figure out how much of each juice I need. Since 2/5 x 5 equals 2, I pour 2 cups of orange juice. Likewise, since 3/5 x 5 equals 3, I pour 3 cups of apple juice. When my kids come in, I gesture towards the cups with a smug smile, confident that my model has predicted the correct outcome.

Then, the unthinkable happens. One of my kids looks at me and says "I don't want apple juice today. May I please have some orange juice?" In despair I pour another cup of orange juice, muttering about the fickleness of children and the failings of science. But is my model wrong? Should I scrap it and give up on this uncertain business of predicting childhood juice consumption?

All Models Are Wrong

There is a popular saying in scientific modeling circles, "All models are wrong...to a certain extent." This means that no matter how much work you spend in trying to refine the equations of your model to make them perfect, there is always a small degree of uncertainty.

If you've ever heard a newscaster report on an election poll, you might have heard one say something like "38% of voters prefer candidate X, plus or minus 5 percent." That "plus or minus 5 percent" is the uncertainty in the model, sometimes called the "margin of error." Taking the uncertainty into account, a more accurate statement would be "somewhere between 32% and 43% of voters prefer candidate X" but that just doesn’t have the same confident ring to it.

Let's Talk About the Weather

So let's go back to the weather. Weather models are a lot more complicated than predicting childhood juice consumption. The equations for weather forecast models incorporate things like wind speed, temperature, geographic features, and many other factors. The equations become so complicated that supercomputers are required to solve them.

Uncertainty in the results comes from the fact that neither the equations themselves nor the input data is perfect. For instance, each instrument that measures temperature has a small margin of error itself. All of these little errors add up until you end up riding home in a 3:00 thunderstorm that was supposed to be a 7:00 light shower.

That isn't to say that we aren't getting better. Every day scientists gather more data, look at the errors of past models, refine their equations, and build more accurate instruments in order to try and reduce the uncertainty.

Conclusion

So now you know how scientists use models to predict the weather, and why they’re so often wrong. This same process applies to each and every prediction that scientists make. Here are the steps they take:

Gather data.

Come up with a set of equations to describe the data.

Use the equations to predict the outcome you can expect to happen with a given input.